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Updated: Sep 4, 2025
06:42
Magnetically Induced Rotating Rayleigh-Taylor Instability
Published on: March 3, 2017
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Network structural origin of instabilities in large complex systems
Chao Duan1,2, Takashi Nishikawa2,3, Deniz Eroglu2,4
1School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
Science Advances
|July 20, 2022
Summary
Network structure, specifically imbalances in links and paths, drives nonnormality and reactivity in complex systems. This understanding helps predict and manage network stability, crucial for power grids and financial networks.
Area of Science:
- Complex network theory
- Systems dynamics
- Network science
Background:
- Large complex networks are vulnerable to dynamical perturbations.
- Nonnormality in networks can lead to reactivity, amplifying responses and causing instabilities.
- Understanding network structure is key to predicting system behavior.
Purpose of the Study:
- Identify structural properties causing nonnormality and reactivity in real-world directed networks.
- Develop a predictive theory for network nonnormality and reactivity.
- Provide insights for network design and management.
Main Methods:
- Analysis of an extensive dataset of real-world directed networks.
- Identification of key network structural properties.
- Development of a quantitative theoretical framework.
Main Results:
- Imbalances in incoming and outgoing links/paths at nodes are identified as key structural properties.
- A theory quantitatively predicting nonnormality and reactivity based on these properties is established.
- The pervasiveness of nonnormality and reactivity in real networks is explained.
Conclusions:
- Network structural imbalances are fundamental drivers of nonnormality and reactivity.
- The developed theory offers predictive power for network behavior.
- Findings can inform strategies for controlling network stability and preventing instabilities.
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